译科技 | 关于边缘计算，你需要了解四件事
来源：venturebeat 时间：2020-04-07 15:03:25 作者：James Falkoff
4 things you need to understand about edge computing
数据观丨王婕（译）
Edge computing has claimed a spot in the technology zeitgeist as one of the topics that signals novelty and cutting-edge thinking. For a few years now，it has been assumed that this way of doing computing is，one way or another，the future. But until recently the discussion has been mostly hypothetical，because the infrastructure required to support edge computing has not been available.
边缘计算作为颇富新颖的前沿思想话题之一，已经在当今技术时代思潮中占据了一席之地。几年来，人们一直认为这种计算方式是未来的发展方向。直到现在，这种论调仍停留在理论阶段，因为能够支撑边缘计算的基础设施尚未出现。
That is now changing as a variety of edge computing resources，from micro data centers to specialized processors to necessary software abstractions，are making their way into the hands of application developers，entrepreneurs，and large enterprises. We can now look beyond the theoretical when answering questions about edge computing’s usefulness and implications. So，what does the real-world evidence tell us about this trend？In particular，is the hype around edge computing deserved，or is it misplaced？
随着各种边缘计算资源（从微数据中心到专业化处理器，再到必要的软件抽象）逐渐涌入应用程序开发人员、企业家和大型企业的手中，这种情况正在发生变化。在回答有关边缘计算的作用和影响的问题时，我们现在可以试着超越理论层面。那么，关于这一趋势，现实世界的案例能给我们什么启示？特别是，围绕边缘计算的炒作究竟是实至名归，还是不合时宜？
Distilled down，the evidence shows that edge computing is a real phenomenon born of a burgeoning need to decentralize applications for cost and performance reasons. Some aspects of edge computing have been over-hyped，while others have gone under the radar. The following four takeaways attempt to give decision makers a pragmatic view of the edge’s capabilities now and in the future.
总的来说，事实表明边缘计算趋势真实存在，它是由于成本和性能原因而产生的一种新兴的去中心化应用程序的需求。边缘计算的某些方面正在被过度炒作，而另一些方面则没有引起应有的注意。以下四个要点旨在帮助决策者对边缘计算现在和未来的能力有一个务实态度。
1. Edge computing isn’t just about latency 边缘计算不仅仅是关于延迟的技术
Edge computing is a paradigm that brings computation and data storage closer to where it is needed. It stands in contrast to the traditional cloud computing model，in which computation is centralized in a handful of hyperscale data centers.The edge can be anywhere that is closer to the end user or device than a traditional cloud data center. It could be 100 miles away，one mile away，on-premises，or on-device. Whatever the approach，the traditional edge computing narrative has emphasized that the power of the edge is to minimize latency，either to improve user experience or to enable new latency-sensitive applications. This does edge computing a disservice. While latency mitigation is an important use case，it is probably not the most valuable one. Another use case for edge computing is to minimize network traffic going to and from the cloud，or what some are calling cloud offload，and this will probably deliver at least as much economic value as latency mitigation.
边缘计算是一种使计算和数据存储得以有的放矢的范例，它与将计算集中在少数几个超大规模的数据中心的传统云计算模型形成鲜明对比。“边缘”可以是指任何比传统云数据中心更接近终端用户或设备的地方，它可能在100英里外、1英里外，甚至是就在现场或设备上。无论采用哪种方法，一般对边缘计算的描述都强调，边缘计算的强大功能是延迟最小化，从而改善用户体验或为启用新的对延迟很敏感的应用程序赋能。但这样的说法的确对人们全面认识边缘计算很不利。虽然对边缘计算来说，缓解延迟是一个重要的应用案例，但它却可能不是最有价值的。边缘计算的另一个应用案例是最小化进出云的网络流量，也就是一些人所说的“云卸载”，这可能会带来至少与缓解延迟一样多的经济价值。
The underlying driver of cloud offload is immense growth in the amount of data being generated，be it by users，devices， or sensors. “Fundamentally， the edge is a data problem，” Chetan Venkatesh，CEO of Macrometa，a startup tackling data challenges in edge computing，told me late last year. Cloud offload has arisen because it costs money to move all this data，and many would rather not move it to if they don’t have to. Edge computing provides a way to extract value from data where it is generated，never moving it beyond the edge. If necessary，the data can be pruned down to a subset that is more economical to send to the cloud for storage or further analysis.
云卸载的潜在驱动力是生成数据量的突飞猛进，无论是在用户、设备还是传感器层面。Macromet公司首席执行官Chetan Venkatesh去年年底曾向作者表示，“从根本上讲，边缘计算是一个数据问题”，云卸载之所以出现，是因为移动数据需要花钱，而且如果没有必要，许多人宁愿不移动它们。边缘计算提供了一种从本地设备直接提取值的方法，因为它不会将数据移到“边缘”之外。如果有必要，可以将数据精简为一个更为经济的子集，并发送到云端进行存储或进一步分析。
A very typical use for cloud offload is to process video or audio data，two of the most bandwidth-hungry data types. A retailer in Asia with 10，000+ locations is processing both，using edge computing for video surveillance and in-store language translation services，according to a contact I spoke to recently who was involved in the deployment. But there are other sources of data that are similarly expensive to transmit to the cloud. According to another contact，a large IT software vendor is analyzing real-time data from its customers’on-premises IT infrastructure to preempt problems and optimize performance. It uses edge computing to avoid backhauling all this data to AWS. Industrial equipment also generates an immense amount of data and is a prime candidate for cloud offload.
云卸载的一个非常典型的用途是处理视频或音频数据——这是两种最需要带宽的数据类型。据我最近接触到的一位参与部署的人士透露，一家在亚洲拥有1万多家分店的零售商正在运用边缘计算技术同时处理这两项业务，以进行视频监控和店内语言翻译服务。但除此之外，还有其他的数据源也需要花费同样多的钱才能传输到云中。另一位知情人士透露，为了防患于未然并优化性能，一家大型IT软件供应商正在分析来自其客户的内部IT基础设施的实时数据，并使用边缘计算来避免将所有这些数据返回到AWS。同时，工业设备也会产生大量的数据，是云卸载技术的主要应用场景。
2. The edge is an extension of the cloud 边缘计算是云计算的延伸
Despite early proclamations that the edge would displace the cloud，it is more accurate to say that the edge expands the reach of the cloud. It will not put a dent in the ongoing trend of workloads migrating to the cloud. But there is a flurry of activity underway to extend the cloud formula of on-demand resource availability and abstraction of physical infrastructure to locations increasingly distant from traditional cloud data centers. These edge locations will be managed using tools and approaches evolved from the cloud，and over time the line between cloud and edge will blur.
尽管在初期有关于边缘计算将取代云计算的说法，但毋宁说是边缘计算拓展了云计算的范围。边缘计算虽然不会对工作负载迁移到云的趋势造成影响，但是，一系列活动正在为了将按需资源可用性和物理基础设施抽象的云计算公式扩展到距离传统云数据中心越来越远的位置而进行。这些边缘位置将使用来自云端的工具和方法进行管理，随着时间的推移，云和边缘之间的界限将变得越来越模糊。
The fact that the edge and the cloud are part of the same continuum is evident in the edge computing initiatives of public cloud providers like AWS and Microsoft Azure. If you are an enterprise looking to do on-premises edge computing，Amazon will now send you an AWS Outpost–a fully assembled rack of compute and storage that mimics the hardware design of Amazon’s own data centers. It is installed in a customer’s own data center and monitored，maintained，and upgraded by Amazon. Importantly，Outposts run many of the same services AWS users have come to rely on，like the EC2 compute service，making the edge operationally similar to the cloud. Microsoft has a similar aim with its Azure Stack Edge product. These offerings send a clear signal that the cloud providers envision cloud and edge infrastructure unified under one umbrella.
边缘计算和云计算是同一连续体的一部分，这一事实在AWS和Microsoft Azure等公共云提供商的边缘计算解决方案中得到了证明。如果你是一家想要部署本地边缘计算的企业，亚马逊将会为你提供一项叫做AWS Outpost的服务——一个模仿了亚马逊自己数据中心的硬件设计的完全组装好的计算机和存储机架。它将被安装在客户自己的数据中心，并由亚马逊进行监控、维护和升级。重要的是，AWS Outpost运行着AWS用户所依赖的许多相同的服务，这使得边缘计算在操作上类似于云计算，例如EC2计算服务。微软的Azure Stack Edge产品也有类似的目标。这些产品都发出了一个明确的信号——云提供商希望将云计算和边缘基础设施统一在同一个“保护伞”下。
3. Edge infrastructure is arriving in phases 边缘计算基础设施正分阶段建设
While some applications are best run on-premises，in many cases application owners would like to reap the benefits of edge computing without having to support any on-premises footprint. This requires access to a new kind of infrastructure，something that looks a lot like the cloud but is much more geographically distributed than the few dozen hyperscale data centers that comprise the cloud today. This kind of infrastructure is just now becoming available，and it’s likely to evolve in three phases，with each phase extending the edge’s reach by means of a wider and wider geographic footprint.
虽然有些应用程序最好在本地运行，但在许多情况下，应用程序所有者希望获得边缘计算的好处，而不必支持任何本地占用。这就需要访问一种新的基础设施，这种基础设施看起来很像云，但在地理分布上要比现在组成云的几十个高级别数据中心分散得多。这种基础设施现在才刚刚开始使用，它可能会分三个阶段发展，每个阶段都会通过越来越广泛的地理足迹扩大边缘计算的覆盖范围。
Phase 1： Multi-Region and Multi-Cloud 阶段一：多区域、多云
The first step toward edge computing for a large swath of applications will be something that many might not consider edge computing，but which can be seen as one end of a spectrum that includes all the edge computing approaches. This step is to leverage multiple regions offered by the public cloud providers. For example，AWS has data centers in 22 geographic regions，with four more announced. An AWS customer serving users in both North America and Europe might run its application in both the Northern California region and the Frankfurt region，for instance. Going from one region to multiple regions can drive a big reduction in latency，and for a large set of applications，this will be all that’s needed to deliver a good user experience.
关于边缘计算的第一步，很多人没有考虑到的是将其应用到大量应用程序当中，但这可以视为所有边缘计算处理的频谱终结。这一步就是利用公共云提供商所提供的多个区域。例如，AWS在22个地理区域拥有数据中心（另有4个已发布），其中，为北美和欧洲用户提供服务的AWS客户就可以在北加州地区和法兰克福地区也运行其应用程序。对于一组大型应用程序来说，从一个区域到多个区域可以大大减少延迟，这将是其提供良好用户体验所需的全部。
At the same time，there is a trend toward multi-cloud approaches，driven by an array of considerations including cost efficiencies，risk mitigation，avoidance of vendor lock-in，and desire to access best-of-breed services offered by different providers. “Doing multi-cloud and getting it right is a very important strategy and architecture today，”Mark Weiner，CMO at distributed cloud startup Volterra，told me. A multi-cloud approach，like a multi-region approach，marks an initial step toward distributed workloads on a spectrum that progresses toward more and more decentralized edge computing approaches.
与此同时，在一系列考虑因素（包括成本效率、风险降低、避免厂商锁定以及希望访问不同提供商提供的最佳服务）的驱动下，出现了向多云方法发展的趋势。分布式云初创公司Volterra的首席营销官Mark Weiner告诉我：“在今天，做多云并把它做好是一个非常重要的战略和架构。”与多区域方法一样，多云方法标志着云计算朝着分布式工作负载迈出了第一步，而分布式工作负载正朝着越来越分散的边缘计算方法发展。
Phase 2： The Regional Edge 阶段2：区域边缘计算
The second phase in the edge’s evolution extends the edge a layer deeper，leveraging infrastructure in hundreds or thousands of locations instead of hyperscale data centers in just a few dozen cities. It turns out there is a set of players who already have an infrastructure footprint like this： Content Delivery Networks. CDNs have been engaged in a precursor to edge computing for two decades now，caching static content closer to end users in order to improve performance. While AWS has 22 regions，a typical CDN like Cloudflare has 194.
边缘计算发展的第二阶段将扩展到更深一层：利用数百或数千个地点的基础设施，而不是几十个城市规模大小的超大数据中心。事实证明，已经有一群玩家拥有了这样的基础设施部署，即内容分发网络。20年来，内容分发网络一直是参与边缘计算发展的先驱，为了提高性能，其将静态内容缓存到更接近终端用户的地方。目前AWS有22个区域部署了这样的基础设施，而像Cloudflare公司这样的典型提供内容分发网络服务的则有194个区域。
What’s different now is these CDNs have begun to open up their infrastructure to general-purpose workloads，not just static content caching. CDNs like Cloudflare，Fastly，Limelight， StackPath，and Zenlayer all offer some combination of container-as-a-service，VM-as-a-service， bare-metal-as-a-service，and serverless functions today. In other words，they are starting to look more like cloud providers. Forward-thinking cloud providers like Packet and Ridge are also offering up this kind of infrastructure，and in turn AWS has taken an initial step toward offering more regionalized infrastructure，introducing the first of what it calls Local Zones in Los Angeles，with additional ones promised.
现在不同的是，这些内容分发网络已经开始向通用工作负载开放其基础架构，而不仅仅是静态内容缓存。如今，像Cloudflare、Fastly、Limelight、StackPath和Zenlayer这样的内容分发网络提供商纷纷开始提供一些容器即服务、虚拟化应用即服务、裸机即服务和无服务器功能的组合。换句话说，他们开始变得更像云提供商。像Packet和Ridge这样具有前瞻性的云提供商也在提供这种基础设施，而AWS也朝着提供更区域化的基础设施迈出了第一步，在洛杉矶引入了第一个它称之为“区域型”的的新公有云服务，并承诺将在更多区域予以部署。
Phase 3： The Access Edge 阶段3：接入边缘计算
The third phase of the edge’s evolution drives the edge even further outward，to the point where it is just one or two network hops away from the end user or device. In traditional telecommunications terminology this is called the Access portion of the network，so this type of architecture has been labeled the Access Edge. The typical form factor for the Access Edge is a micro data center，which could range in size from a single rack to roughly that of a semi trailer，and could be deployed on the side of the road or at the base of a cellular network tower，for example. Behind the scenes，innovations in things like power and cooling are enabling higher and higher densities of infrastructure to be deployed in these small-footprint data centers.
在向前发展的第三个阶段，边缘计算将进一步向外驱动，直到距离终端用户或设备只有一两个网络跳数。在传统的电信术语中，这被称为网络的接入部分，因此这种类型的架构被标记为接入边缘。接入边缘的典型组成因素是微型数据中心，其大小可以从单个机架到大致相当于半拖车的机架，并且可以部署在路边或蜂窝网络塔的底部。在这背后，电力和冷却等方面的创新将使得越来越高密度的基础设施能够部署在这些占地面积小的数据中心。
New entrants such as Vapor IO，EdgeMicro，and EdgePresence have begun to build these micro data centers in a handful of US cities. 2019 was the first major buildout year，and 2020– 2021 will see continued heavy investment in these buildouts. By 2022，edge data center returns will be in focus for those who made the capital investments in them，and ultimately these returns will reflect the answer to the question：are there enough killer apps for bringing the edge this close to the end user or device？
像Vapor IO、EdgeMicro和EdgePresence公司这样的新晋者已经开始在美国少数城市建立这些微型数据中心。2019年是这些微型数据中心的建设元年，2020-2021年对这些建设的投资将持续加大。到2022年，边缘数据中心的回报率将成为那些对边缘计算进行资本投资的人所关注的焦点，最终这些回报率将反映出一个问题的答案：是否有足够的杀手级应用程序可以让边缘计算如此接近终端用户或设备？
We are very early in the process of getting an answer to this question. A number of practitioners I’ve spoken to recently have been skeptical that the micro data centers in the Access Edge are justified by enough marginal benefit over the regional data centers of the Regional Edge. The Regional Edge is already being leveraged in many ways by early adopters，including for a variety of cloud offload use cases as well as latency mitigation in user-experience-sensitive domains like online gaming，ad serving，and e-commerce. By contrast，the applications that need the super-low latencies and very short network routes of the Access Edge tend to sound further off：autonomous vehicles，drones，AR/VR，smart cities，remote-guided surgery. More crucially，these applications must weigh the benefits of the Access Edge against doing the computation locally with an on-premises or on-device approach. However，a killer application for the Access Edge could certainly emerge–perhaps one that is not in the spotlight today. We will know more in a few years.
我们很早就得到了这个问题的答案。我最近采访过的一些从业者对接入边缘的微型数据中心是否比区域边缘的区域数据中心更具有足够的边际效益表示怀疑。区域边缘计算已经在许多方面被早期采用者利用，包括各种云卸载案例，以及对在线游戏、广告服务和电子商务等用户体验敏感领域的延迟缓解。相比之下，那些需要超低延迟和非常短距离的接入边缘网络路径的应用程序往往听起来更加遥不可及：自动驾驶、无人机、AR/VR、智能城市、远程手术。更重要的是，这些应用程序必须权衡接入边缘的好处，而不是使用本地或设备上的方法进行本地计算。然而，访问边缘的一个杀手级应用程序肯定会出现——也许这不是今天的焦点。几年后我们会知道更多。
4.New software is needed to manage the edge 需要新的软件来管理边缘计算
I’ve outlined above how edge computing describes a variety of architectures and that the “edge”can be located in many places. However，the ultimate direction of the industry is one of unification，toward a world in which the same tools and processes can be used to manage cloud and edge workloads regardless of where the edge resides. This will require the evolution of the software used to deploy，scale，and manage applications in the cloud，which has historically been architected with a single data center in mind.
在上文中我概述了边缘计算是如何描述各种体系结构，以及“边缘”可以位于许多地方。然而，这一行业的最终方向是走向统一，走向一个可以使用相同的工具和流程来管理云和边缘工作负载的世界，而不管边缘位于何处。这将需要对用于在云上部署、扩展和管理应用程序的软件进行改进，而云上的应用程序在过去是以单个数据中心为架构的。
Startups such as Ori，Rafay Systems，and Volterra，and big company initiatives like Google’s Anthos，Microsoft’s Azure Arc，and VMware’s Tanzu are evolving cloud infrastructure software in this way. Virtually all of these products have a common denominator：They are based on Kubernetes，which has emerged as the dominant approach to managing containerized applications. But these products move beyond the initial design of Kubernetes to support a new world of distributed fleets of Kubernetes clusters. These clusters may sit atop heterogeneous pools of infrastructure comprising the “edge，”on-premises environments，and public clouds，but thanks to these products they can all be managed uniformly.
像Ori、Rafay Systems和Volterra等初创公司，以及Google的antos、Microsoft的Azure Arc和VMware的Tanzu等大公司都在以这种方式发展云基础设施软件。几乎所有这些产品都有一个共同点：它们基于Kubernetes进行开发，这一技术已经成为管理集装箱化应用程序的主要方法。但这些产品已超越了Kubernetes最初支持一个由Kubernetes集群组成的分布式机群新世界的设计初衷。这些集群可能位于由“边缘”、内部部署环境和公共云组成的异构基础设施池之上，但由于这些产品的出现，它们都可以被统一管理。
kubernetes，简称K8s，是用8代替8个字符“ubernete”而成的缩写。是一个开源的，用于管理云平台中多个主机上的容器化的应用，Kubernetes的目标是让部署容器化的应用简单并且高效（powerful），Kubernetes提供了应用部署，规划，更新，维护的一种机制。
Initially，the biggest opportunity for these offerings will be in supporting Phase 1 of the edge’s evolution，i.e. moderately distributed deployments that leverage a handful of regions across one or more clouds. But this puts them in a good position to support the evolution to the more distributed edge computing architectures beginning to appear on the horizon. “Solve the multi-cluster management and operations problem today and you’re in a good position to address the broader edge computing use cases as they mature，”Haseeb Budhani，CEO of Rafay Systems，told me recently.
最初，这些产品的最大机会将是支持边缘计算发展的第一阶段，即通过一个或多个云利用少量区域的适度分布式部署。但这恰好使它们处于有利地位，可以支持即将来临的更加分布式的边缘计算架构的演进。Rafay Systems的首席执行官Haseeb Budhani最近告诉我：“解决当今多集群管理和操作问题，你就可以在更广泛的边缘计算案例成熟时解决它们。”
On the edge of something great 伟大与边缘之间
Now that the resources to support edge computing are emerging，edge-oriented thinking will become more prevalent among those who design and support applications. Following an era in which the defining trend was centralization in a small number of cloud data centers，there is now a countervailing force in favor of increased decentralization. Edge computing is still in the very early stages，but it has moved beyond the theoretical and into the practical. And one thing we know is this industry moves quickly. The cloud as we know it is only 14 years old. In the grand scheme of things，it will not be long before the edge has left a big mark on the computing landscape.
如今支持边缘计算的资源正方兴未艾，边缘思维将在设计和支持应用程序的人员中变得更加流行。在一个以计算资源集中在少数云数据中心为典型趋势的时代之后，目前出现了一股支持进一步分散的抵消力量。边缘计算仍然处于非常早期的阶段，但是它已经超越了理论而进入了实践。在如今云计算只有14年的历史背景下，我们能感受到这个行业的发展是如此之快。从长远来看，用不了多久，边缘计算将势必在计算机领域留下浓墨重彩的一笔。
注：《关于边缘计算，你需要了解四件事》来源于venturebeat网站（点击查看原文）。本文系数据观原创编译，译者数据观/王婕，转载请务必注明译者和来源。
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